A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation
Computing in Science and Engineering
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images
International Journal of Computer Vision
Digital Signal Processing
A novel approach to image segmentation using improved watershed transformation
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Fast segmentation of bone in CT images using 3D adaptive thresholding
Computers in Biology and Medicine
Size distribution estimation of stone fragments via digital image processing
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Segmentation of the liver using the deformable contour method on CT images
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
A novel model of image segmentation based on watershed algorithm
Advances in Multimedia
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In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.